The neurofibromatoses (NFs), including NF1, NF2, and schwannomatosis, are a group of autosomal-dominant neurogenetic disorders characterized by a predisposition in virtually 100% of patients to develop multiple nerve sheath tumors. The determination of tumor burden on magnetic resonance imaging (MRI) images is crucial for the management of NF patients, which is a life-long follow-up for the monitoring of tumor progression and the assessment of treatment responses. However, volumetric tumor quantification is not a clinical routine for the longitudinal management of NF patients because of the technical challenges in the accurate and efficient segmentation of highly-irregular and infiltrating NF tumors in particular plexiform neurofibromas, and the less attention and financial restriction to the development of this specialized software. In this project, we propose an innovative technical solution: cloud quantitative imaging (CQI) for NF quantification, denoted as CQI-NF, which will provide the volumetric quantification of NF tumors on whole-body and regional MRI images via virtualization (cloud computing) technology for NF clinics nationwide and worldwide without the high cost to develop and maintain on-site advanced quantitative imaging software and hardware. This project will be built upon existing technologies for quantitative imaging analysis developed at the 3D Imaging Lab at Massachusetts General Hospital (MGH). It will make use of the core technology, dynamic-thresholding level set (DT level set), developed by the MGH research team for accurate segmentation of plexiform neurofibromas on MRI images. Project collaborators include neuro-oncologists specialized in NF management at the MGH NF Clinic, musculoskeletal radiologists specialized in NF diagnosis on MRI images, and imaging scientists specializing in quantitative imaging analysis from the MGH 3D Imaging Lab. The specific aims of the project are: (1) Development of CQI-NF system: We will develop the prototype CQI-NF system on a cloud-computing platform, including a parallel DT level set method for segmentation of NF tumors on MRI images, a cloud-based scheme for quantification of NF tumors including segmentation, interactive contouring, tumor burden analysis, visualization, and point-of-care data access for the longitudinal management of NF patients. (2) Evaluation of CQI-NF system: We will retrospectively collect three groups of MRI NF cases including 50 whole-body MRI cases at the MGH NF Clinic, 40 multiple-site cases from our four collaborative NF clinics outside MGH, and 10 multiple-time cases over the last 10 years at MGH. We will conduct a clinical study to evaluate the software accuracy, interobserver repeatability, cloud-interaction performance, regional and whole-body MRI agreement, and software reliability of the proposed CQI-NF system. (3) Plan of project Phase II: Following on this Phase I project, we will prepare the Phase II application, and plan the software development, clinical re-evaluation and commercialization in Phase II.